Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations1192
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory111.9 KiB
Average record size in memory96.1 B

Variable types

Numeric12

Alerts

alcohol is highly overall correlated with qualityHigh correlation
citric acid is highly overall correlated with fixed acidity and 1 other fieldsHigh correlation
density is highly overall correlated with fixed acidityHigh correlation
fixed acidity is highly overall correlated with citric acid and 2 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
pH is highly overall correlated with fixed acidityHigh correlation
quality is highly overall correlated with alcoholHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxideHigh correlation
volatile acidity is highly overall correlated with citric acidHigh correlation

Reproduction

Analysis started2025-01-07 11:00:26.608976
Analysis finished2025-01-07 11:00:41.057513
Duration14.45 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

High correlation 

Distinct91
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4231544
Minimum4.7
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2025-01-07T12:00:41.135319image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4.7
5-th percentile6.2
Q17.2
median8
Q39.4
95-th percentile11.9
Maximum15
Range10.3
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.7101468
Coefficient of variation (CV)0.20302926
Kurtosis0.32846856
Mean8.4231544
Median Absolute Deviation (MAD)1
Skewness0.78285548
Sum10040.4
Variance2.924602
MonotonicityNot monotonic
2025-01-07T12:00:41.261306image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 44
 
3.7%
7.8 39
 
3.3%
7.6 37
 
3.1%
7 37
 
3.1%
7.5 37
 
3.1%
8 36
 
3.0%
7.7 35
 
2.9%
8.2 34
 
2.9%
7.1 33
 
2.8%
7.9 33
 
2.8%
Other values (81) 827
69.4%
ValueCountFrequency (%)
4.7 1
 
0.1%
5 5
0.4%
5.1 1
 
0.1%
5.2 3
0.3%
5.3 4
0.3%
5.4 4
0.3%
5.5 1
 
0.1%
5.6 6
0.5%
5.7 1
 
0.1%
5.8 4
0.3%
ValueCountFrequency (%)
15 1
 
0.1%
14.3 1
 
0.1%
14 1
 
0.1%
13.8 1
 
0.1%
13.7 1
 
0.1%
13.5 1
 
0.1%
13.4 1
 
0.1%
13.3 3
0.3%
13.2 2
0.2%
13 2
0.2%

volatile acidity
Real number (ℝ)

High correlation 

Distinct136
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51388423
Minimum0.12
Maximum1.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2025-01-07T12:00:41.386319image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.27
Q10.38
median0.5
Q30.62
95-th percentile0.84
Maximum1.18
Range1.06
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.17473001
Coefficient of variation (CV)0.34001824
Kurtosis0.32644259
Mean0.51388423
Median Absolute Deviation (MAD)0.12
Skewness0.60245629
Sum612.55
Variance0.030530576
MonotonicityNot monotonic
2025-01-07T12:00:41.522825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 35
 
2.9%
0.5 34
 
2.9%
0.43 32
 
2.7%
0.58 32
 
2.7%
0.38 31
 
2.6%
0.6 30
 
2.5%
0.39 29
 
2.4%
0.42 27
 
2.3%
0.56 27
 
2.3%
0.36 26
 
2.2%
Other values (126) 889
74.6%
ValueCountFrequency (%)
0.12 1
 
0.1%
0.16 2
 
0.2%
0.18 7
0.6%
0.19 2
 
0.2%
0.2 2
 
0.2%
0.21 5
0.4%
0.22 5
0.4%
0.23 5
0.4%
0.24 11
0.9%
0.25 7
0.6%
ValueCountFrequency (%)
1.18 1
 
0.1%
1.13 1
 
0.1%
1.115 1
 
0.1%
1.09 1
 
0.1%
1.07 1
 
0.1%
1.04 3
0.3%
1.035 1
 
0.1%
1.025 1
 
0.1%
1.02 3
0.3%
1.01 1
 
0.1%

citric acid
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29202181
Minimum0.01
Maximum0.79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2025-01-07T12:00:41.662293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.02
Q10.13
median0.28
Q30.44
95-th percentile0.5945
Maximum0.79
Range0.78
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.18074068
Coefficient of variation (CV)0.6189287
Kurtosis-0.84860347
Mean0.29202181
Median Absolute Deviation (MAD)0.15
Skewness0.24290172
Sum348.09
Variance0.032667193
MonotonicityNot monotonic
2025-01-07T12:00:41.805619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.49 57
 
4.8%
0.24 40
 
3.4%
0.02 36
 
3.0%
0.08 32
 
2.7%
0.1 29
 
2.4%
0.26 29
 
2.4%
0.4 27
 
2.3%
0.01 25
 
2.1%
0.09 25
 
2.1%
0.42 25
 
2.1%
Other values (67) 867
72.7%
ValueCountFrequency (%)
0.01 25
2.1%
0.02 36
3.0%
0.03 24
2.0%
0.04 24
2.0%
0.05 18
1.5%
0.06 20
1.7%
0.07 17
1.4%
0.08 32
2.7%
0.09 25
2.1%
0.1 29
2.4%
ValueCountFrequency (%)
0.79 1
 
0.1%
0.76 1
 
0.1%
0.75 1
 
0.1%
0.74 3
0.3%
0.73 2
0.2%
0.72 1
 
0.1%
0.71 1
 
0.1%
0.7 1
 
0.1%
0.69 3
0.3%
0.68 3
0.3%

residual sugar
Real number (ℝ)

Distinct75
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4417785
Minimum0.9
Maximum8.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2025-01-07T12:00:41.942207image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.6
Q11.9
median2.2
Q32.6
95-th percentile4.445
Maximum8.1
Range7.2
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.9577995
Coefficient of variation (CV)0.39225486
Kurtosis8.563988
Mean2.4417785
Median Absolute Deviation (MAD)0.3
Skewness2.6388779
Sum2910.6
Variance0.91737987
MonotonicityNot monotonic
2025-01-07T12:00:42.078438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 121
 
10.2%
2.2 95
 
8.0%
2.1 94
 
7.9%
1.8 93
 
7.8%
2.3 80
 
6.7%
1.9 80
 
6.7%
2.4 69
 
5.8%
2.5 67
 
5.6%
2.6 64
 
5.4%
1.7 51
 
4.3%
Other values (65) 378
31.7%
ValueCountFrequency (%)
0.9 1
 
0.1%
1.2 3
 
0.3%
1.3 4
 
0.3%
1.4 21
 
1.8%
1.5 25
 
2.1%
1.6 49
4.1%
1.65 1
 
0.1%
1.7 51
4.3%
1.75 2
 
0.2%
1.8 93
7.8%
ValueCountFrequency (%)
8.1 1
0.1%
7.9 1
0.1%
7.8 2
0.2%
7.3 1
0.1%
7.2 1
0.1%
7 1
0.1%
6.7 1
0.1%
6.6 2
0.2%
6.55 1
0.1%
6.4 2
0.2%

chlorides
Real number (ℝ)

Distinct126
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.082775168
Minimum0.012
Maximum0.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2025-01-07T12:00:42.313905image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.012
5-th percentile0.054
Q10.07
median0.079
Q30.089
95-th percentile0.119
Maximum0.27
Range0.258
Interquartile range (IQR)0.019

Descriptive statistics

Standard deviation0.025651343
Coefficient of variation (CV)0.30989176
Kurtosis14.206173
Mean0.082775168
Median Absolute Deviation (MAD)0.009
Skewness2.95343
Sum98.668
Variance0.00065799139
MonotonicityNot monotonic
2025-01-07T12:00:42.446535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 45
 
3.8%
0.078 43
 
3.6%
0.074 41
 
3.4%
0.076 37
 
3.1%
0.079 36
 
3.0%
0.082 35
 
2.9%
0.071 34
 
2.9%
0.075 34
 
2.9%
0.077 34
 
2.9%
0.084 34
 
2.9%
Other values (116) 819
68.7%
ValueCountFrequency (%)
0.012 1
 
0.1%
0.038 2
0.2%
0.039 4
0.3%
0.041 1
 
0.1%
0.042 2
0.2%
0.043 1
 
0.1%
0.044 3
0.3%
0.045 4
0.3%
0.046 4
0.3%
0.047 3
0.3%
ValueCountFrequency (%)
0.27 1
0.1%
0.263 1
0.1%
0.25 1
0.1%
0.243 1
0.1%
0.241 1
0.1%
0.236 1
0.1%
0.23 1
0.1%
0.226 1
0.1%
0.222 1
0.1%
0.216 1
0.1%

free sulfur dioxide
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.73406
Minimum1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2025-01-07T12:00:42.568698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median13
Q321
95-th percentile35
Maximum57
Range56
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.051388
Coefficient of variation (CV)0.63882986
Kurtosis0.75620959
Mean15.73406
Median Absolute Deviation (MAD)7
Skewness1.0194887
Sum18755
Variance101.03039
MonotonicityNot monotonic
2025-01-07T12:00:42.691893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 105
 
8.8%
5 81
 
6.8%
10 59
 
4.9%
12 58
 
4.9%
15 57
 
4.8%
7 53
 
4.4%
9 48
 
4.0%
13 44
 
3.7%
17 44
 
3.7%
11 43
 
3.6%
Other values (45) 600
50.3%
ValueCountFrequency (%)
1 2
 
0.2%
2 1
 
0.1%
3 32
 
2.7%
4 32
 
2.7%
5 81
6.8%
5.5 1
 
0.1%
6 105
8.8%
7 53
4.4%
8 39
 
3.3%
9 48
4.0%
ValueCountFrequency (%)
57 1
 
0.1%
54 1
 
0.1%
53 1
 
0.1%
52 3
0.3%
51 2
0.2%
50 1
 
0.1%
48 1
 
0.1%
47 1
 
0.1%
46 1
 
0.1%
45 3
0.3%

total sulfur dioxide
Real number (ℝ)

High correlation 

Distinct139
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.191275
Minimum6
Maximum165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2025-01-07T12:00:42.816425image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q122
median38
Q364
95-th percentile112.45
Maximum165
Range159
Interquartile range (IQR)42

Descriptive statistics

Standard deviation32.292565
Coefficient of variation (CV)0.68429099
Kurtosis0.71171922
Mean47.191275
Median Absolute Deviation (MAD)19
Skewness1.1195219
Sum56252
Variance1042.8097
MonotonicityNot monotonic
2025-01-07T12:00:42.950628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 31
 
2.6%
24 29
 
2.4%
15 27
 
2.3%
20 27
 
2.3%
19 25
 
2.1%
27 24
 
2.0%
18 23
 
1.9%
23 22
 
1.8%
14 22
 
1.8%
25 22
 
1.8%
Other values (129) 940
78.9%
ValueCountFrequency (%)
6 2
 
0.2%
7 4
 
0.3%
8 9
 
0.8%
9 10
 
0.8%
10 20
1.7%
11 17
1.4%
12 19
1.6%
13 20
1.7%
14 22
1.8%
15 27
2.3%
ValueCountFrequency (%)
165 1
 
0.1%
155 1
 
0.1%
153 1
 
0.1%
152 1
 
0.1%
151 1
 
0.1%
149 1
 
0.1%
148 1
 
0.1%
147 2
0.2%
145 3
0.3%
144 3
0.3%

density
Real number (ℝ)

High correlation 

Distinct397
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99674737
Minimum0.99007
Maximum1.00289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2025-01-07T12:00:43.086619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.99007
5-th percentile0.9937265
Q10.9956475
median0.996745
Q30.9978625
95-th percentile0.9998
Maximum1.00289
Range0.01282
Interquartile range (IQR)0.002215

Descriptive statistics

Standard deviation0.0018012808
Coefficient of variation (CV)0.0018071589
Kurtosis0.59524367
Mean0.99674737
Median Absolute Deviation (MAD)0.001115
Skewness-0.038349002
Sum1188.1229
Variance3.2446127 × 10-6
MonotonicityNot monotonic
2025-01-07T12:00:43.227649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9968 28
 
2.3%
0.9976 26
 
2.2%
0.998 26
 
2.2%
0.9972 23
 
1.9%
0.9962 22
 
1.8%
0.997 21
 
1.8%
0.9982 20
 
1.7%
0.9978 19
 
1.6%
0.9984 18
 
1.5%
0.9964 17
 
1.4%
Other values (387) 972
81.5%
ValueCountFrequency (%)
0.99007 1
0.1%
0.9902 1
0.1%
0.99064 1
0.1%
0.9908 1
0.1%
0.99084 1
0.1%
0.9912 1
0.1%
0.9915 1
0.1%
0.9917 1
0.1%
0.99182 2
0.2%
0.9922 1
0.1%
ValueCountFrequency (%)
1.00289 1
 
0.1%
1.0026 1
 
0.1%
1.0022 1
 
0.1%
1.0021 1
 
0.1%
1.0018 1
 
0.1%
1.0014 5
0.4%
1.001 6
0.5%
1.0008 3
0.3%
1.0006 5
0.4%
1.0004 7
0.6%

pH
Real number (ℝ)

High correlation 

Distinct83
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3017198
Minimum2.86
Maximum3.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2025-01-07T12:00:43.361383image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.86
5-th percentile3.08
Q13.21
median3.3
Q33.39
95-th percentile3.54
Maximum3.85
Range0.99
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.14250657
Coefficient of variation (CV)0.043161316
Kurtosis0.36083843
Mean3.3017198
Median Absolute Deviation (MAD)0.09
Skewness0.17579463
Sum3935.65
Variance0.020308123
MonotonicityNot monotonic
2025-01-07T12:00:43.489388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 44
 
3.7%
3.26 43
 
3.6%
3.34 38
 
3.2%
3.32 37
 
3.1%
3.36 37
 
3.1%
3.28 37
 
3.1%
3.38 36
 
3.0%
3.39 35
 
2.9%
3.22 34
 
2.9%
3.31 33
 
2.8%
Other values (73) 818
68.6%
ValueCountFrequency (%)
2.86 1
 
0.1%
2.87 1
 
0.1%
2.88 2
0.2%
2.89 2
0.2%
2.92 1
 
0.1%
2.94 2
0.2%
2.98 4
0.3%
2.99 2
0.2%
3 3
0.3%
3.01 2
0.2%
ValueCountFrequency (%)
3.85 1
 
0.1%
3.78 1
 
0.1%
3.75 1
 
0.1%
3.74 1
 
0.1%
3.72 2
0.2%
3.71 1
 
0.1%
3.7 1
 
0.1%
3.69 2
0.2%
3.68 1
 
0.1%
3.67 3
0.3%

sulphates
Real number (ℝ)

Distinct83
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6517953
Minimum0.33
Maximum1.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2025-01-07T12:00:43.615958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile0.47
Q10.55
median0.62
Q30.73
95-th percentile0.91
Maximum1.36
Range1.03
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.14262255
Coefficient of variation (CV)0.21881493
Kurtosis2.3703479
Mean0.6517953
Median Absolute Deviation (MAD)0.08
Skewness1.2238895
Sum776.94
Variance0.020341191
MonotonicityNot monotonic
2025-01-07T12:00:43.750485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.54 53
 
4.4%
0.62 50
 
4.2%
0.6 48
 
4.0%
0.58 48
 
4.0%
0.57 46
 
3.9%
0.56 41
 
3.4%
0.59 39
 
3.3%
0.61 39
 
3.3%
0.53 38
 
3.2%
0.64 36
 
3.0%
Other values (73) 754
63.3%
ValueCountFrequency (%)
0.33 1
 
0.1%
0.37 1
 
0.1%
0.39 3
 
0.3%
0.4 2
 
0.2%
0.42 4
 
0.3%
0.43 8
0.7%
0.44 11
0.9%
0.45 5
 
0.4%
0.46 11
0.9%
0.47 15
1.3%
ValueCountFrequency (%)
1.36 3
0.3%
1.22 1
 
0.1%
1.2 1
 
0.1%
1.18 3
0.3%
1.17 1
 
0.1%
1.16 1
 
0.1%
1.14 1
 
0.1%
1.13 1
 
0.1%
1.12 1
 
0.1%
1.11 1
 
0.1%

alcohol
Real number (ℝ)

High correlation 

Distinct63
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.431222
Minimum8.4
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2025-01-07T12:00:43.870837image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9.2
Q19.5
median10.2
Q311.1
95-th percentile12.5
Maximum14
Range5.6
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.0629564
Coefficient of variation (CV)0.10190143
Kurtosis-0.013248062
Mean10.431222
Median Absolute Deviation (MAD)0.7
Skewness0.81057912
Sum12434.017
Variance1.1298764
MonotonicityNot monotonic
2025-01-07T12:00:43.995047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 100
 
8.4%
9.4 73
 
6.1%
9.8 56
 
4.7%
9.2 56
 
4.7%
10 53
 
4.4%
10.5 50
 
4.2%
9.3 48
 
4.0%
11 44
 
3.7%
9.6 43
 
3.6%
9.7 42
 
3.5%
Other values (53) 627
52.6%
ValueCountFrequency (%)
8.4 2
 
0.2%
8.5 1
 
0.1%
8.7 2
 
0.2%
9 18
 
1.5%
9.05 1
 
0.1%
9.1 17
 
1.4%
9.2 56
4.7%
9.233333333 1
 
0.1%
9.25 1
 
0.1%
9.3 48
4.0%
ValueCountFrequency (%)
14 4
0.3%
13.6 3
0.3%
13.56666667 1
 
0.1%
13.5 1
 
0.1%
13.4 3
0.3%
13.3 3
0.3%
13.2 1
 
0.1%
13.1 1
 
0.1%
13 5
0.4%
12.9 6
0.5%

quality
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6526846
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2025-01-07T12:00:44.100013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.82173017
Coefficient of variation (CV)0.1453699
Kurtosis0.26802548
Mean5.6526846
Median Absolute Deviation (MAD)1
Skewness0.25447317
Sum6738
Variance0.67524048
MonotonicityNot monotonic
2025-01-07T12:00:44.196328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 502
42.1%
6 471
39.5%
7 155
 
13.0%
4 40
 
3.4%
8 17
 
1.4%
3 7
 
0.6%
ValueCountFrequency (%)
3 7
 
0.6%
4 40
 
3.4%
5 502
42.1%
6 471
39.5%
7 155
 
13.0%
8 17
 
1.4%
ValueCountFrequency (%)
8 17
 
1.4%
7 155
 
13.0%
6 471
39.5%
5 502
42.1%
4 40
 
3.4%
3 7
 
0.6%

Interactions

2025-01-07T12:00:39.693464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:26.930997image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:28.067852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:29.200551image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:30.368556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:31.593704image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:32.691767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:33.847402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:35.143100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:36.282687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:37.347116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:38.441928image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:39.778201image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:27.014479image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:28.163853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:29.297702image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:30.460635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:31.679939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:32.776769image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:33.943165image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:35.233070image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:36.363716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:37.435086image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:38.531931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:39.866137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:27.099873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-07T12:00:29.392122image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:30.552938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:31.768916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:32.866951image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:34.039706image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:35.325317image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:36.452685image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:37.522291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:38.624175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:39.966471image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:27.197829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-07T12:00:29.500639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:30.658447image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:31.865887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:32.966366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:34.142887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:35.430526image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:36.547868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:37.619795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:38.721504image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:40.058522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:27.289305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:28.465335image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:29.598644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:30.752901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:31.962916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:33.060255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:34.243924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-07T12:00:37.806768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-07T12:00:28.649587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:29.787951image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:31.028176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:32.142133image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-07T12:00:32.241167image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-07T12:00:28.844810image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-07T12:00:32.343606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:33.467400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:34.666711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:35.916520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:37.009005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:38.094143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:39.328495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:40.509857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-07T12:00:28.930939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:30.087607image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:31.318587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:32.424835image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:33.553400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-07T12:00:37.090337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:38.176815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-07T12:00:40.596888image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-07T12:00:31.410808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:32.517839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-07T12:00:37.176531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:38.264920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:39.515016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:40.686448image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:27.980601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:29.112337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:30.276520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:31.500699image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:32.603731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:33.746402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:35.053893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:36.190522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:37.261878image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:38.353525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-07T12:00:39.607242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-07T12:00:44.278296image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
alcoholchloridescitric aciddensityfixed acidityfree sulfur dioxidepHqualityresidual sugarsulphatestotal sulfur dioxidevolatile acidity
alcohol1.000-0.2710.154-0.441-0.013-0.1120.1440.5130.1230.278-0.306-0.250
chlorides-0.2711.0000.0940.4220.2370.021-0.216-0.1980.237-0.0130.1710.167
citric acid0.1540.0941.0000.3480.665-0.100-0.4930.2340.1670.330-0.072-0.593
density-0.4410.4220.3481.0000.626-0.031-0.310-0.1870.4170.1400.1390.053
fixed acidity-0.0130.2370.6650.6261.000-0.172-0.7030.1210.2280.214-0.118-0.272
free sulfur dioxide-0.1120.021-0.100-0.031-0.1721.0000.128-0.0860.0680.0210.7870.038
pH0.144-0.216-0.493-0.310-0.7030.1281.000-0.038-0.088-0.0380.0280.193
quality0.513-0.1980.234-0.1870.121-0.086-0.0381.0000.0300.413-0.245-0.392
residual sugar0.1230.2370.1670.4170.2280.068-0.0880.0301.0000.0570.1330.065
sulphates0.278-0.0130.3300.1400.2140.021-0.0380.4130.0571.000-0.060-0.331
total sulfur dioxide-0.3060.171-0.0720.139-0.1180.7870.028-0.2450.133-0.0601.0000.169
volatile acidity-0.2500.167-0.5930.053-0.2720.0380.193-0.3920.065-0.3310.1691.000

Missing values

2025-01-07T12:00:40.807663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-07T12:00:40.983221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
07.80.760.042.30.09215.054.00.99703.260.659.85
111.20.280.561.90.07517.060.00.99803.160.589.86
27.90.600.061.60.06915.059.00.99643.300.469.45
37.80.580.022.00.0739.018.00.99683.360.579.57
47.50.500.366.10.07117.0102.00.99783.350.8010.55
56.70.580.081.80.09715.065.00.99593.280.549.25
68.90.620.183.80.17652.0145.00.99863.160.889.25
78.90.620.193.90.17051.0148.00.99863.170.939.25
88.50.280.561.80.09235.0103.00.99693.300.7510.57
97.40.590.084.40.0866.029.00.99743.380.509.04
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
11827.20.6600.332.50.06834.0102.00.994143.270.7812.86
11836.60.7250.207.80.07329.079.00.997703.290.549.25
11846.30.5500.151.80.07726.035.00.993143.320.8211.66
11855.40.7400.091.70.08916.026.00.994023.670.5611.66
11866.30.5100.132.30.07629.040.00.995743.420.7511.06
11876.80.6200.081.90.06828.038.00.996513.420.829.56
11886.20.6000.082.00.09032.044.00.994903.450.5810.55
11895.90.5500.102.20.06239.051.00.995123.520.7611.26
11905.90.6450.122.00.07532.044.00.995473.570.7110.25
11916.00.3100.473.60.06718.042.00.995493.390.6611.06